Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization (2501.13017v1)

Abstract: Head-related transfer functions (HRTFs) with dense spatial grids are desired for immersive binaural audio generation, but their recording is time-consuming. Although HRTF spatial upsampling has shown remarkable progress with neural fields, spatial upsampling only from a few measured directions, e.g., 3 or 5 measurements, is still challenging. To tackle this problem, we propose a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject from a dataset. The HRTF of the retrieved subject at the desired direction is fed into the neural field in addition to the sound source direction itself. Furthermore, we present a neural network that can efficiently handle multiple retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate. Our experiments confirm the benefits of RANF on the SONICOM dataset, and it is a key component in the winning solution of Task 2 of the listener acoustic personalization challenge 2024.

• The paper's main contribution is the development of RANF, which integrates external HRTF data into neural fields to predict high-resolution HRTFs from sparse measurements.
• The methodology employs a transform-average-concatenate mechanism that efficiently fuses retrievals, outperforming traditional upsampling methods on the SONICOM dataset.
• Practically, the model offers cost-effective spatial audio personalization and paves the way for future research on dynamic auditory environments and enhanced retrieval strategies.

Overview of Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization

The paper presents a novel approach to the challenges of head-related transfer function (HRTF) upsampling and personalization through the use of a retrieval-augmented neural field (RANF). High-resolution HRTFs are critical for applications in immersive spatial audio, yet they require extensive measurement efforts. Traditional methods for spatial upsampling, needing a limited number of measured directions, strive to strike a balance between accuracy and practicality. The introduction of RANF seeks to enhance these systems by integrating retrieval-augmented generation techniques.

Key Contributions

The authors propose the RANF model, which augments the neural field with external HRTF data from a dataset comprising multiple subjects. The model selects a subset of subjects whose HRTFs are closest in similarity to those of a given target subject. The selected HRTFs from the dataset are then integrated into the neural field model, enhancing its ability to predict HRTFs for the target subject from sparse measurements. The paper further enhances the model’s structure by introducing a transform-average-concatenate mechanism, allowing efficient fusion of information from multiple retrievals.

Numerical Results

The efficacy of RANF is validated on the SONICOM dataset, utilizing the listener acoustic personalization (LAP) challenge metrics as evaluation criteria. In cases where only sparse measurements were available (e.g., from 3, 5, 19, or 100 directions), RANF demonstrated superior HRTF upsampling performance over traditional NFs and HRTF selection methods. For instance, RANF showed a notable reduction in log-spectral distortion (LSD) and interaural time difference (ITD) error compared to the baselines, particularly in settings with an extremely limited number of measurements.

Implications and Future Directions

Practically, RANF’s enhancement of HRTF predictability with sparse measurements offers substantial benefits for developing cost-effective spatial audio solutions without compromising auditory quality. Theoretically, the integration of retrieval-augmented generation methodologies into HRTF personalization may inspire similar cross-disciplinary applications where data scarcity exists but can be mitigated by leveraging related datasets.

This paper outlines a path for further exploration in AI-driven audio personalization. Future research could explore the extension of RANF to handle dynamic and moving sound environments and further refinement of retrieval strategies to increase adaptability and improve performance under diverse auditory conditions. Additionally, the relationship between the diversity of HRTF datasets and the accuracy of retrieval processes merits further paper.

In conclusion, the paper makes significant contributions to the field of HRTF personalization by employing sophisticated integration of retrieval mechanisms into neural network models, paving the way for more personalized and efficient spatial audio technologies.